The novel CFRG -based complex fuzzy transfer learning system

Abstract

Today, the rapid development of the internet has led to a data explosion; the complex fuzzy transfer learning (CFTL) model has received increasing attention from the academic community due to its various real-world applications, such as solar activity, digital signal processing, time series forecasting, etc. CFTL combines Transfer learning and Complex Fuzzy Logic in a framework to solve the problem of learning tasks with no prior direct contextual knowledge, which is stored, retrieved, and organized in the data structure. Data structures play an important role in computational intelligence because they are key performance indicators for systems or models. Therefore, to improve the performance of the previous CFTL, this paper investigates a novel complex fuzzy decision tree (CFDT) structure to represent the complex fuzzy rules and provides a transfer learning model for a complex fuzzy inference system. In contrast with prior axis-parallel decision trees in which only a single feature or variable is considered at each node, the node of the proposed decision tree structures includes complex fuzzy inference rules that contain multiple elements. Multiple features for each node help minimize the size. To prove the efficiency of the proposed framework, we carry out extension experiments on numerous instances (datasets). Experimental results demonstrate/exhibit that our offered performs better than the prior framework regarding accuracy and the size of the produced trees.